Obtain age-standardized health impacts

This function obtains age-standardized health impacts based on multiple age-group specific assessments

Usage

standardize(output_attribute, age_group, ref_prop_pop = NULL)

Arguments

output_attribute: List containing the output of one single healthiar::attribute_...() call in which the age groups of interest were entered in the argument age_group, i.e. one assessment stratified by age group and not a list of age group-specific assessments.
age_group: String vector with the age groups included in the age standardization. The vector refers to age-dependent data in this function and to output_attribute (if provided).
ref_prop_pop: Numeric vector specifying with the reference proportion of population for each age group. If this argument is empty, the proportion of population by age group in the provided data will be used.

Value

This function returns a list containing:

1) health_main (tibble) containing the main results. The direct method of standardization applies the age group-specific rates observed in the study population to a reference population distribution. It therefore standardizes rates and not counts:

impact_per_100k_inhab and bhd_per_100k_inhab (numeric columns) are age-standardized, i.e. the age group-specific rates weighted with ref_prop_pop;
pop_fraction (numeric column) is the ratio of these two age-standardized rates, i.e. the age-standardized attributable fraction;
impact, bhd and population (numeric columns) are the crude totals across the age groups, and exp (numeric column) the population-weighted mean exposure. They are not standardized: a standardized count would require the absolute size of the reference population, while ref_prop_pop provides only its age distribution.

Note that all results are identical to the crude ones if ref_prop_pop is not entered, because in that case the age distribution of the study population itself is taken as reference.

2) health_detailed (tibble) containing the results per age group, including the interim columns of the standardization. The columns ending in _std are the contribution of each age group and add up to the corresponding column of health_main, i.e. sum(impact_per_100k_inhab_std), sum(exp_std) and sum(pop_fraction_std).

Details

Methodology

This function applies the direct method of standardization, where the age-specific rates observed in a study population are applied to a standard (reference) population distribution.

For age standardization in health impact assessments, the World Health Organization (Ahmad et al. 2001) and the Global Burden of Disease study (GBD 2019 Demographics Collaborators 2020) provide the relevant information on this topic.

Detailed information about the methodology (including equations) is available in the package vignette. More specifically, see chapters:

Standardization

This function works after running attribute_health() or attribute_lifetable() functions.

Combination with compare()

Age-standardized results cannot currently be compared with compare(). compare() reads the results by age group of each assessment, i.e. health_detailed$results_raw, which standardize() passes on unchanged: the standardization is in health_main and in health_detailed$impact_std_by_age_group, so applying compare() to the output of standardize() gives exactly the same result as applying it to the assessments themselves. The other way round does not work either, because a comparison has one exposure and one population per scenario and therefore none of the columns that standardize() needs. To compare two scenarios in terms of age-standardized rates, apply standardize() to each of them and compare the resulting health_main$impact_per_100k_inhab directly.

References

Ahmad OB, Pinto CB, Lopez AD, Murray CJ, Lozano R, Inoue M (2001). “Age standardization of rates: a new WHO standard.” Technical Report GPE Discussion Paper Series: No. 31, World Health Organization, Geneva.

GBD 2019 Demographics Collaborators (2020). “Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019.” The Lancet, 396(10258), 1160-1203. doi:10.1016/S0140-6736(20)30977-6 .

Author

Alberto Castro & Axel Luyten

Examples

# Goal: age-standardize two age group-specific impacts
output_attribute <- attribute_health(
  rr_central = 1.063,
  rr_increment = 10,
  erf_shape = "log_linear",
  cutoff_central =  0,
  age_group = c("below_40", "above_40"),
  exp_central = c(8.1, 10.9),
  bhd_central = c(1000, 4000),
  population = c(100000, 500000)
)
results <- standardize(
  output_attribute = output_attribute,
  age_group = c("below_40", "above_40"),
  ref_prop_pop = c(0.5, 0.5)
)
results$health_detailed$results_raw$impact_per_100k_inhab # age group-specific impact rate
#> [1] 48.28250 51.53977
results$health_main$impact_per_100k_inhab # age-standardized impact rate
#> [1] 49.91113